Vision Transformers Explained: 4 Architectures Powering AI Video
From ViT to Swin Transformer, these four architectures revolutionized how AI processes visual information—and they're the backbone of today's deepfake generators and detectors alike.
Taalas Hardwired AI Chips Hit 17K Tokens Per Second
Startup Taalas is challenging GPU dominance with hardwired AI chips designed specifically for inference, claiming 17,000 tokens per second throughput for ubiquitous AI deployment.
ADAPT: Hybrid Prompt Optimization Advances LLM Interpretability
New research introduces ADAPT, a hybrid optimization technique that combines discrete and continuous methods to visualize and understand internal features of large language models.
JAX-Privacy: Differential Privacy Library for ML Training
New JAX-based library enables differentially private training of machine learning models, addressing critical privacy concerns in AI development and synthetic media generation.
Influence-Preserving Proxies Accelerate LLM Fine-Tuning Data Sele
New research introduces proxy methods that preserve gradient influence signals while dramatically reducing computational costs for selecting optimal training data in large language model fine-tuning.
Variability Modeling Meets LLMs: Tuning Inference Parameters
New research applies software product line variability modeling to systematically optimize LLM inference hyperparameters like temperature and sampling strategies.
MIRA: Combining Memory Systems with RL for Smarter AI Agents
New research introduces MIRA, a framework that integrates memory architectures with reinforcement learning while minimizing expensive LLM calls, advancing efficient autonomous agent design.
2025 AI Agent Index: Mapping Safety Features Across Deployed Syst
New research systematically documents technical and safety features across deployed agentic AI systems, creating a comprehensive index for understanding how autonomous AI operates in the wild.
Can Parameter Region Constraints Make LLMs Safer?
New research explores whether constraining specific parameter regions in large language models can ensure safety, examining the theoretical foundations of alignment through architectural constraints.
Agentic Unlearning: Teaching AI Agents to Forget Responsibly
New research explores machine unlearning for LLM agents, addressing how autonomous AI systems can selectively forget data while maintaining tool-use and reasoning capabilities.
IARPA TrojAI Program: Detecting Hidden Backdoors in AI Models
IARPA's TrojAI program releases final report on detecting trojan attacks in AI systems, covering image classifiers, NLP models, and reinforcement learning with implications for synthetic media security.
World Models: The Technical Foundation Behind AI Video Generation
From cognitive science mental simulators to Sora's video generation, world models represent AI's ability to predict and simulate reality—the core technology powering synthetic media.